39 citations · 71 across the 22 of their papers we have counts for
23 papers
InCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring
Hao Yang, Yanyan Zhao, Kewei Zhao +11
Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction. However, existi…
ENPMR-Bench: Benchmarking Proactive Memory Retrieval for Emotional Support Agents
Xing Fu, Yulin Hu, Mengtong Ji +5
Memory-augmented language agents are increasingly deployed in affective applications such as emotional support, where understanding and responding to users' latent emotional needs…
OP-Bench: Benchmarking Over-Personalization for Memory-Augmented Personalized Conversational Agents
Yulin Hu, Zimo Long, Jiahe Guo +5
Memory-augmented conversational agents enable personalized interactions using long-term user memory and have gained substantial traction. However, existing benchmarks primarily foc…
STAR-S: Improving Safety Alignment through Self-Taught Reasoning on Safety Rules
Di Wu, Yanyan Zhao, Xin Lu +2
Defending against jailbreak attacks is crucial for the safe deployment of Large Language Models (LLMs). Recent research has attempted to improve safety by training models to reason…
TEA-Bench: A Systematic Benchmarking of Tool-enhanced Emotional Support Dialogue Agent
Xingyu Sui, Yanyan Zhao, Yulin Hu +3
Emotional Support Conversation requires not only affective expression but also grounded instrumental support to provide trustworthy guidance. However, existing ESC systems and benc…
When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents
Jiahe Guo, Xiangran Guo, Yulin Hu +8
Long-term memory enables large language model (LLM) agents to support personalized and sustained interactions. However, most work on personalized agents prioritizes utility and use…
CARE-Bench: A Benchmark of Diverse Client Simulations Guided by Expert Principles for Evaluating LLMs in Psychological Counseling
Bichen Wang, Yixin Sun, Junzhe Wang +6
The mismatch between the growing demand for psychological counseling and the limited availability of services has motivated research into the application of Large Language Models (…
Psychological Counseling Cannot Be Achieved Overnight: Automated Psychological Counseling Through Multi-Session Conversations
Junzhe Wang, Bichen Wang, Xing Fu +3
In recent years, Large Language Models (LLMs) have made significant progress in automated psychological counseling. However, current research focuses on single-session counseling,…
How Does Sequence Modeling Architecture Influence Base Capabilities of Pre-trained Language Models? Exploring Key Architecture Design Principles to Avoid Base Capabilities Degradation
Xin Lu, Yanyan Zhao, Si Wei +3
Pre-trained language models represented by the Transformer have been proven to possess strong base capabilities, and the representative self-attention mechanism in the Transformer…
Separate the Wheat from the Chaff: A Post-Hoc Approach to Safety Re-Alignment for Fine-Tuned Language Models
Di Wu, Xin Lu, Yanyan Zhao +1
Although large language models (LLMs) achieve effective safety alignment at the time of release, they still face various safety challenges. A key issue is that fine-tuning often co…
RKLD: Reverse KL-Divergence-based Knowledge Distillation for Unlearning Personal Information in Large Language Models
Bichen Wang, Yuzhe Zi, Yixin Sun +2
With the passage of the Right to Be Forgotten (RTBF) regulations and the scaling up of language model training datasets, research on model unlearning in large language models (LLMs…
Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey
Hao Yang, Yanyan Zhao, Yang Wu +6
Compared to traditional sentiment analysis, which only considers text, multimodal sentiment analysis needs to consider emotional signals from multimodal sources simultaneously and…
Towards Comprehensive Post Safety Alignment of Large Language Models via Safety Patching
Weixiang Zhao, Yulin Hu, Zhuojun Li +7
Safety alignment of large language models (LLMs) has been gaining increasing attention. However, current safety-aligned LLMs suffer from the fragile and imbalanced safety mechanism…
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers
Xin Lu, Yanyan Zhao, Bing Qin +3
Pre-trained language models have been proven to possess strong base capabilities, which not only excel in in-distribution language modeling but also show powerful abilities in out-…
Improving the Downstream Performance of Mixture-of-Experts Transformers via Weak Vanilla Transformers
Xin Lu, Yanyan Zhao, Bing Qin +1
Recently, Mixture of Experts (MoE) Transformers have garnered increasing attention due to their advantages in model capacity and computational efficiency. However, studies have ind…
Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence
Weixiang Zhao, Zhuojun Li, Shilong Wang +5
Emotional Intelligence (EI), consisting of emotion perception, emotion cognition and emotion expression, plays the critical roles in improving user interaction experience for the c…
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models
Weixiang Zhao, Shilong Wang, Yulin Hu +6
The continual learning (CL) ability is vital for deploying large language models (LLMs) in the dynamic world. Existing methods devise the learning module to acquire task-specific k…
An Early Evaluation of GPT-4V(ision)
Yang Wu, Shilong Wang, Hao Yang +4
In this paper, we evaluate different abilities of GPT-4V including visual understanding, language understanding, visual puzzle solving, and understanding of other modalities such a…
Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion
Tao He, Ming Liu, Yixin Cao +4
Sparse knowledge graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods, that is, the completion performance decreases rapidly with the increas…
UNIMO-3: Multi-granularity Interaction for Vision-Language Representation Learning
Hao Yang, Can Gao, Hao Líu +3
Vision-and-language (VL) pre-training, which aims to learn a general representation of image-text pairs that can be transferred to various vision-and-language tasks. Compared with…
Improving Cross-Task Generalization with Step-by-Step Instructions
Yang Wu, Yanyan Zhao, Zhongyang Li +2
Instruction tuning has been shown to be able to improve cross-task generalization of language models. However, it is still challenging for language models to complete the target ta…
TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition
Weixiang Zhao, Yanyan Zhao, Shilong Wang +1
Emotion Support Conversation (ESC) is an emerging and challenging task with the goal of reducing the emotional distress of people. Previous attempts fail to maintain smooth transit…
Is ChatGPT Equipped with Emotional Dialogue Capabilities?
Weixiang Zhao, Yanyan Zhao, Xin Lu +3
This report presents a study on the emotional dialogue capability of ChatGPT, an advanced language model developed by OpenAI. The study evaluates the performance of ChatGPT on emot…